Unsupervised Machine Learning Analysis Using Clustering Model
🗺️This notebook includes the following:
- Preprocessing
- Data cleaning
- Exploratory data analysis (EDA)
- Principal component analysis (PCA)
- Clustering analysis
🗺️Objective:
The goal of this project is to cluster countries based on various numerical features such as economic indicators, health factors, and social parameters. This is an unsupervised learning problem because we do not have any predefined categories or labels for the countries. We need to use machine learning techniques to create clusters of similar countries based on their features. The resulting clusters will help in the allocation of funds for assistance during natural disasters and calamities, as countries with similar characteristics are likely to face similar challenges during such events.
Kaggle link: Unsupervised Learning on Country Data
📔About Dataset
Clustering the Countries by Using Unsupervised Learning for HELP International
📔Objective:
To categorize the countries using socio-economic and health factors that determine the overall development of the country.
📔About organization:
HELP International is an international humanitarian NGO that is committed to fighting poverty and providing the people of backward countries with basic amenities and relief during a time of disasters and natural calamities.
📔Problem Statement:
HELP International has been able to raise around $ 10 million. Now the CEO of the NGO needs to decide how to use this money strategically and effectively. So, the CEO has to make the decision to choose the countries that are in direst need of aid. Hence, your Job as a Data scientist is to categorize the countries using some socio-economic and health factors that determine the overall development of the country. Then you need to suggest the countries in which the CEO needs to focus the most.